Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8–10, 2023, Guangzhou, China

Research Article

Exploration of Machine Learning Applications in Systemic Financial Risk Prediction and Management

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  • @INPROCEEDINGS{10.4108/eai.8-12-2023.2344705,
        author={Keyu  Yao},
        title={Exploration of Machine Learning Applications in Systemic Financial Risk Prediction and Management},
        proceedings={Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8--10, 2023, Guangzhou, China},
        publisher={EAI},
        proceedings_a={MSIEID},
        year={2024},
        month={4},
        keywords={financial risk management machine learning techniques transparency systemic risk warning deep learning},
        doi={10.4108/eai.8-12-2023.2344705}
    }
    
  • Keyu Yao
    Year: 2024
    Exploration of Machine Learning Applications in Systemic Financial Risk Prediction and Management
    MSIEID
    EAI
    DOI: 10.4108/eai.8-12-2023.2344705
Keyu Yao1,*
  • 1: University of Sheffield
*Contact email: wellington589125@gmail.com

Abstract

In this multidisciplinary study, we explore the transformative impact of machine learning (ML) technologies in financial research. Our objective is to understand how supervised and unsupervised learning methods can be applied to tasks such as fraud detection, asset price forecasting, financial risk assessment, and the development of early warning systems for systemic financial risk. We adopt a variety of algorithms—including Backpropagation Neural Networks, Bayesian Networks, Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), Random Forest, and XGBoost—and evaluate their ability to analyze and predict outcomes from complex financial data. Our methodology entails a comparative analysis of ML techniques against traditional statistical methods, particularly in their handling of imbalanced datasets.